New Assessment Method on Shear Resistance of Perfobond Shear Connectors in Steel-Concrete Composite Structure

摘要

The behaviour of shear connectors will have a major impact on the global behavior of steel-concreterncomposite structure. Perfobond shear connector has been developed during last two decades and hasrnbecome popular due to their advantageous properties. Longitudinal shear strength is considered as arnmajor constraint in the design of composite structure and it can be assessed by expensive and timernconsuming experimental techniques. Longitudinal shear resistance of perfobond shear connectors isrndepends on some design parameters. In this paper, considering its powerful prediction ability onrnnonlinear problem, artificial neural networks was induced to investigating and a new intelligentrnevaluation method on shear resistance of perfobond shear connectors was proposed. Choosing therndiameter of holes, the yield strength of steel plate, concrete compressive strength, the ratio ofrntransverse rebar, depth of steel plate and thick of steel plate as input values, Back PropagationrnNeural Networks (BPNN) model was developed for determination of the longitudinal shearrnresistance of perfobond shear connectors. It is demonstrated that, with the same design parametersrnas test specimens, the longitudinal shear resistance generated by the BPNN model is quite close torntest result after proper training of the BPNN. Furthermore, Instead of three-dimensional FEMrnAnalysis or Push-out test, the BPNN model is computationally efficient tool used to predict shearrnresistance of perfobond shear connectors in different parameters.